In recent years, the rapid development of computer vision and computer graphics technologies has resulted in the revolutionary development of spatial information industries worldwide, and the traditional two-dimensional (2D) plane spatial information are now gradually replaced by the three-dimensional (3D) visualization applications and 3D space analysis. Accordingly, a trend of presenting a particular spatial object by use of 3D geological information plus visualization of a real 3D scene has arisen in the geological information system, and especially, establishment of 3D building models has now become one of the hottest research topics.
In the process of establishing a 3D building model, one of the most difficult problems is how to process image data of the real 3D scene effectively. Generally speaking, in order to establish a complete, real and large-scale 3D building model, the image data of the real 3D scene must be analyzed at first, then a building texture necessary for establishing the 3D building model is identified and extracted, and finally the extracted building texture is mapped onto the 3D building model through a mapping process to accomplish visualization of the real 3D scene.
Aerial images and panoramic images are two primary kinds of image data necessary for establishing a 3D building model. However, conventional technologies cannot analyze the aerial images and panoramic images efficiently so as to identify and extract building textures necessary for establishing a 3D building model. For example, in most of the conventional technologies, a plurality of aerial images are taken at different orientations and a plurality of panoramic images are taken at different orientations, and then building textures of the images are projected manually onto a 3D building model. However, this way of processing makes the mapping process very complicated, time-consuming and costly; furthermore, because the building texture of each building is formed from a plurality of images taken at different orientations, it is difficult to determine which images shall be kept or discarded, thus causing the building in the 3D building model to have non-uniform tones. Therefore, the conventional technologies not only fail to process the aerial images and panoramic images efficiently, but also make the mapping process very complicated and necessitate beautification and correction of the model subsequent to the mapping process.
In view of this, an urgent need exists in the art to provide a solution that can correctly identify and extract a suitable building texture of a building by effectively analyzing an aerial image and a panoramic image of the building so as to solve the problem of the conventional technologies.